Performance of embankments with and without reinforcement on soft subsoil
Bibliographic record
Abstract
A case history of both reinforced and unreinforced embankments on soft subsoil built-to-failure is described and analyzed. The effect of geotextile reinforcements on embankment behavior is discussed by comparing the field and numerical analysis results of cases with and without reinforcement. The results of a laboratory model test on the behavior of embankments on soft subsoil are discussed. Both field and laboratory tests, as well as analysis results, indicate that the reinforcement had a positive effect on embankment stability. However, at a working state (for a factor of safety of FS = 1.2~1.3) the reinforcement did not have an obvious effect on the subsoil response. The effect of reinforcement on subsoil deformation could be noticed only when the unreinforced embankment was close to failure. The laboratory model test results indicated that if the reinforcement is stiff and strong enough, the effect of reinforcement is considerable. It is suggested that although the geotextile has a beneficial effect on embankment over soft subsoil due to its relative lower stiffness, to achieve a substantial improvement on embankment behavior, the stiffer and stronger reinforcements should be used. This case history also demonstrated that the rate of lateral displacement and excess pore pressure development are sensitive indicators of the stability of embankment on soft subsoil.Key words: embankment, reinforcement, soft ground, field tests, laboratory tests, FEM analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".